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Universal features of correlated bursty behaviour
Scientific Reports
|May 8, 2012
Summary
Temporal processes in human communication, neuron activity, and seismic signals exhibit bursty intervals. A new method using event counts in bursts reveals universal power-law distributions and memory effects across these phenomena.
Area of Science:
- Complex systems analysis
- Statistical physics
- Time series analysis
Background:
- Inhomogeneous temporal processes, common in human communication, neural activity, and seismic signals, feature alternating high-activity bursts and low-activity periods.
- Existing characterizations using fat-tailed inter-event time distributions and autocorrelation functions inadequately capture complex temporal correlations.
- These limitations necessitate novel approaches to fully understand the dynamics of bursty temporal phenomena.
Purpose of the Study:
- To identify a more effective metric for characterizing temporal correlations in bursty processes.
- To demonstrate the universality of observed patterns across diverse systems.
- To propose a unifying model for understanding memory effects in these phenomena.
Main Methods:
- Analysis of the distribution of the number of events within bursty periods.
- Comparison of findings across disparate systems like human communications, neuron spike trains, and seismic signals.
- Development and application of a simple phenomenological model incorporating memory effects.
Main Results:
- The distribution of event counts within bursts serves as a robust indicator of underlying dependencies.
- A universal power-law distribution is observed for the number of events in bursts across various phenomena.
- Temporal correlations in these systems can be explained by memory effects, consistent with the proposed model.
Conclusions:
- The number of events in a bursty period is a key characteristic for understanding temporal dependencies.
- A universal power-law behavior in bursty processes suggests common underlying mechanisms.
- Memory effects provide a unifying explanation for temporal correlations in diverse inhomogeneous processes.
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